Manufacturing Engineering

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Browsing Manufacturing Engineering by Author "Henok Zewdu (Mr.) Co-Advisor"

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    Development and Characterization of the Mechanical Properties of Al2024 alloy Reinforced with SiO2 and Bagasse Ash Composite
    (Addis Ababa University, 2021-07) Senait Menbere; Shantha Kumar (Assoc. Prof.); Henok Zewdu (Mr.) Co-Advisor
    Silica sand and sugarcane bagasse-ash (BA) are used as reinforcement for Aluminum alloy (Al2024) based hybrid composites. The Aluminum matrix hybrid composites were fabricated by stir cast at 750°C. The reinforcement weighted by volume fraction ratio 5%, 10% & 15% of matrix and reinforcements, <63μm particle size, stirring the slurry at 650rpm for 10 minutes were the parameters used for the fabrications of aluminum matrix hybrid composites. The development and experimental findings of aluminum matrix hybrid composite mechanical properties were performed by adding silica sand and bagasse ash as reinforcement with different composition proportions. The addition of this reinforcement with nine compositions proportion, in range of 5-15% with 5% interval. For a maximum improvement of the material properties, solution heat-treatment (T3-temper) was performed. The effects of the reinforcements have been examined through different mechanical tests. These tests were implemented using Rockwell hardness indenter, bending, and tensile strength by universal testing machines, and optical microscopy was used to characterize the microstructure of composite specimens. Specimens were prepared as per the ASTM E18-15 for Rockwell hardness and E8M/16a for tensile, E290 for bending test specimen standards. The samples are modeled using Solid works 2017, and its analysis was performed by ANSYS 19.2. The analysis result showed a higher effect of the reinforcing bagasse-ash with different compositions in aluminum matrix reinforced composites. Enhanced mechanical properties have been achieved in the 3rd case compared to the 1st and the 2nd BA & SiO2 combination. It shows that the selection of BA & SiO2 as reinforcement has one of the essential criteria for fabricating aluminum matrix reinforced composites. The result showed that the hardness of the composites increased slightly with an increase in bagasse ash content with a maximum increment of 15%. The maximum mechanical properties were observed for the Al2024 reinforced composite at 5% bagasse ash and10% silicon dioxide compositions. Tensile strength increased to a maximum value of 560MPa, and also flexural strength increased to a maximum value of 482MPa at 10% SiO2 and 5% bagasse ash compositions. Hybrid composite superior properties are observed in tensile strength, flexural strength, and hardness than single reinforced Al2024/ SiO2 metal matrix composites. Also, for the application of fuselage-skin panels, the reinforced and heat-treated Al2024/SiO2/BA-T3 improved a better stress resistance performance than the unreinforced Al2024- T3/T351 through FEA.
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    Predicting Sand Casting Defects using a Data-Driven Supervised Machine Learning Approach: A Case Study of Akaki Basic Metals Industry
    (Addis Ababa University, 2024-06) Demewez Demeke; Mesfin Gizaw (PhD); Henok Zewdu (Mr.) Co-Advisor
    This research investigates supervised machine learning to predict sand casting defects and its severity, aiming to enhance product quality and reduce costs in metal casting. Effective quality control is essential for maintaining structural integrity, energy efficiency, and environmental sustainability. Defects such as porosities, inclusions, shrinkages, cracks, and blowholes increase energy consumption and environmental impact. Rework, scrap, and product rejection due to defects gain significant production costs and reduce profitability. The study identifies and mitigates defects in bronze, steel, and cast iron products weighing from 15 kg to 16,800 kg. Using a dataset of 1001 samples with 37 features, it evaluates machine learning algorithms: Decision Tree, K-Nearest Neighbors, Gradient Boosting, Random Forest, XGBoost, SVC, Ensemble methods, and NN. XGBoost is most effective, with 87% accuracy in defect type prediction and 94% in severity classification. Specifically, the ensemble XGBoost model achieves 93.07% accuracy in defect severity and 86.67% in defect types. The Neural Network also performs well but shows signs of overfitting due to the small dataset. Severity is classified into severe, minor, and moderate; defect types include non-defect, porosity, shrinkage, and others (misrun, blowhole inclusion, crack, and metal penetration). User-friendly tools based on these models are accessible via URLs (https://scdp-dt.streamlit.app/ and https://scdp-severity.streamlit.app/), aiding defect assessment and decision-making in sand casting. In conclusion, machine learning enhances operational efficiency and product quality while promoting sustainability. It also reduces energy use, minimizes rework costs, and enhances quality control, aligning with global environmental goals.